Deep Learning-Based Defect Detection Framework for Ultra High Resolution Images of Tunnels

Abstract

This study proposes a defect detection framework to improve the performance of deep learning-based detection models for ultra-high resolution (UHR) images generated by tunnel inspection systems. Most of the scanning technologies used in tunnel inspection systems generate UHR images. Defects in real-world images, on the other hand, are noticeably smaller than the image. These characteristics make simple preprocessing applications, such as downscaling, difficult due to information loss. Additionally, when a deep learning model is trained by the UHR images under the limited computational resource for training, problems may occur, including a reduction in object detection rate, unstable training, etc. To address these problems, we propose a framework that includes preprocessing and postprocessing of UHR images related to image patches rather than focusing on deep learning models. Furthermore, it includes a method for supplementing problems according to the format of the data annotation in the preprocessing process. When the proposed framework was applied to the UHR images of a tunnel, the performance of the deep learning-based defect detection model was improved by approximately 77.19 percentage points (pp). Because the proposed framework is for general UHR images, it can effectively recognize damage to general structures other than tunnels. Thus, it is necessary to verify the applicability of the defect detection framework under various conditions in future works.

Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases

“…Deep learning is an important improvement in machine learning for GPR data. With the rapid development of computer technology, deep learning algorithms, which rely on powerful computing power, have been vigorously developed for NDT infrastructure health monitoring [ 15 , 16 , 17 , 18 ]. CNN and RNN have the ability to learn the data structure information, and the dependencies contained between data elements have obvious superiority in target recognition and classification [ 19 , 20 ].…”